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# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from types import MethodType
from typing import TYPE_CHECKING, Optional, Union
import torch
from transformers import Trainer
from typing_extensions import override
from ...extras import logging
from ...extras.packages import is_transformers_version_greater_than
from ..callbacks import FixValueHeadModelCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
if TYPE_CHECKING:
from transformers import PreTrainedModel, ProcessorMixin
from transformers.trainer import PredictionOutput
from ...hparams import FinetuningArguments
logger = logging.get_logger(__name__)
class PairwiseTrainer(Trainer):
r"""Inherits Trainer to compute pairwise loss."""
def __init__(
self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
) -> None:
if is_transformers_version_greater_than("4.46"):
kwargs["processing_class"] = kwargs.pop("tokenizer")
super().__init__(**kwargs)
self.model_accepts_loss_kwargs = False # overwrite trainer's default behavior
self.finetuning_args = finetuning_args
self.can_return_loss = True # override property to return eval_loss
self.add_callback(FixValueHeadModelCallback)
if processor is not None:
self.add_callback(SaveProcessorCallback(processor))
if finetuning_args.use_badam:
from badam import BAdamCallback, clip_grad_norm_old_version # type: ignore
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
self.add_callback(BAdamCallback)
@override
def create_optimizer(self) -> "torch.optim.Optimizer":
if self.optimizer is None:
self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
return super().create_optimizer()
@override
def create_scheduler(
self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
) -> "torch.optim.lr_scheduler.LRScheduler":
create_custom_scheduler(self.args, num_training_steps, optimizer)
return super().create_scheduler(num_training_steps, optimizer)
@override
def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
return super()._get_train_sampler()
@override
def compute_loss(
self, model: "PreTrainedModel", inputs: dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
) -> Union["torch.Tensor", tuple["torch.Tensor", list["torch.Tensor"]]]:
r"""Compute pairwise loss. The first n examples are chosen and the last n examples are rejected.
Subclass and override to inject custom behavior.
Note that the first element will be removed from the output tuple.
See: https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer.py#L3842
"""
_, _, values = model(**inputs, output_hidden_states=True, return_dict=True, use_cache=False)
batch_size = inputs["input_ids"].size(0) // 2
chosen_masks, rejected_masks = torch.split(inputs["attention_mask"], batch_size, dim=0)
chosen_rewards, rejected_rewards = torch.split(values, batch_size, dim=0)
chosen_scores = chosen_rewards.gather(dim=-1, index=(chosen_masks.sum(dim=-1, keepdim=True) - 1))
rejected_scores = rejected_rewards.gather(dim=-1, index=(rejected_masks.sum(dim=-1, keepdim=True) - 1))
chosen_scores, rejected_scores = chosen_scores.squeeze(), rejected_scores.squeeze()
loss = -torch.nn.functional.logsigmoid(chosen_scores.float() - rejected_scores.float()).mean()
if return_outputs:
return loss, (loss, chosen_scores, rejected_scores)
else:
return loss
def save_predictions(self, predict_results: "PredictionOutput") -> None:
r"""Save model predictions to `output_dir`.
A custom behavior that not contained in Seq2SeqTrainer.
"""
if not self.is_world_process_zero():
return
output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
logger.info_rank0(f"Saving prediction results to {output_prediction_file}")
chosen_scores, rejected_scores = predict_results.predictions
with open(output_prediction_file, "w", encoding="utf-8") as writer:
res: list[str] = []
for c_score, r_score in zip(chosen_scores, rejected_scores):
res.append(json.dumps({"chosen": round(float(c_score), 2), "rejected": round(float(r_score), 2)}))
writer.write("\n".join(res))
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